Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
Pandas TimedeltaIndex.isna() function detect all the missing values from the given TimedeltaIndex object.
Syntax : TimedeltaIndex.isna()
Parameters : None
Return : array object
Example #1: Use TimedeltaIndex.isna() function to detect all the missing values from the given TimedeltaIndex object.
# importing pandas as pd import pandas as pd   # Create the TimedeltaIndex object tidx = pd.TimedeltaIndex(data =['06:05:01.000030', None, '22 day 2 min 3us 10ns',                                    '+23:59:59.999999', None, '+12:19:59.999999'])   # Print the TimedeltaIndex object print(tidx) |
Output :
Now we will use the TimedeltaIndex.isna() function to detect all the missing values from the tidx object.
# find all missing values tidx.isna() |
Output :
As we can see in the output, the TimedeltaIndex.isna() function has returned an array object which contains True value corresponding to the missing values.
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Example #2: Use TimedeltaIndex.isna() function to detect all the missing values from the given TimedeltaIndex object.
# importing pandas as pd import pandas as pd   # Create the TimedeltaIndex object tidx = pd.TimedeltaIndex(data =[None, '1 days 06:05:01.000030', None,                        '1 days 02:00:00', '21 days 06:15:01.000030'])   # Print the TimedeltaIndex object print(tidx) |
Output :
Now we will use the TimedeltaIndex.isna() function to detect all the missing values from the tidx object.
# find all missing values tidx.isna() |
Output :
As we can see in the output, the TimedeltaIndex.isna() function has returned an array object which contains True value corresponding to the missing values.
